ICTs, Distributed Discourse and the Labour Movement in Cabo Verde: Why Weak Communications Remain a Crucial Barrier to Trade Union Effectiveness
Bibliographic record
Abstract
Based on interviews with trade union officials from different islands and the Inspector General of Labour, this article examines the use of information and communication technologies (ICTs) by labour organisations in Cabo Verde. Distributed discourse is used as a conceptual framework to provide insights into ways in which the ability of trade unionists to engage in debates and formulate initiatives is influenced by the pervasiveness and control of digital technologies. ICTs are premised as complementary tools and not as substitutes for existing face-to-face union communication strategies, a perspective substantiated by all the interviewees. The research reveals that frail communication channels are major problems for unions in Cabo Verde, which significantly impede their ability to defend the rights of members effectively. Despite localised improvements, involving particular organisations, ICTs are not being utilised systematically and equitably across all the Cabo Verdean islands to enhance the effectiveness of the work of unions. Limited financial streams, high levels of informality in the labour force, a dispersed geography, the uneven penetration of digital technologies across islands and economic sectors, and government policies are major barriers to trade union communication. Policy implications are put forward in the light of the main research findings. KEYWORDS: trade unions; Cabo Verde; ICTs; distributed discourse; labour democracy
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".